In many contexts, it is extremely costly to perform enough high-quality experimental measurements to accurately parametrize a predictive quantitative model. However, it is often much easier to carry out large numbers of experiments that indicate whether each sample is above or below a given threshold. Can many such categorical or “coarse” measurements be combined with a much smaller number of high-resolution or “fine” measurements to yield accurate models? Here, we demonstrate an intuitive strategy, inspired by statistical physics, wherein the coarse measurements are used to identify the salient features of the data, while the fine measurements determine the relative importance of these features. A linear model is inferred from the fine measurements, augmented by a quadratic term that captures the correlation structure of the coarse data. We illustrate our strategy by considering the problems of predicting the antimalarial potency and aqueous solubility of small organic molecules from their 2D molecular structure.

中文翻译：

---

粗测与细测相结合的实验优化设计

在许多情况下，执行足够的高质量实验测量以准确地参数化预测性定量模型的成本非常高。但是，进行大量表明每个样品是高于还是低于给定阈值的实验通常要容易得多。可以将许多此类分类或“粗略”测量结果与少量的高分辨率或“精细”测量结果结合起来以产生准确的模型吗？在这里，我们展示了一种受统计物理学启发的直观策略，其中粗略的度量用于识别数据的显着特征，而精细的度量确定这些特征的相对重要性。从精细测量推断出线性模型，并用捕获粗数据相关结构的二次项进行扩充。